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Identification of a variable mass underwater vehicle via volterra neural network / T. Binazadeh in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 132 N° 2 (Mars/Avril 2010) 

Public ISBD [article]  in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 132 N° 2 (Mars/Avril 2010) . - 07 p. Titre : Identification of a variable mass underwater vehicle via volterra neural network Type de document : texte imprimé Auteurs : T. Binazadeh, Auteur ; Yazdanpanah, M. J., Auteur ; M. H. Shafiei, Auteur Année de publication : 2010 Article en page(s) : 07 p. Note générale : Systèmes dynamiques Langues : Anglais (eng) Mots-clés : Hydrodynamics  Mechanical  engineering  computing  Neural  nets  Polynomial  approximation  Recursive  estimation  Underwater  vehicles  Vehicle  dynamics  Volterra  equations Index. décimale : 629.8 Résumé : The first step in designing a control system for a rigid body is to understand its dynamics. Underwater vehicle dynamics may be complex and difficult to model, mainly due to difficulties in observing and measuring actual underwater vehicle hydrodynamics response. This paper is concerned with structure selection of nonlinear polynomials in a Volterra polynomial basis function neural network and recursive parameter estimation of the selected model, in order to obtain a model of a variable mass underwater vehicle with six degrees of freedom using an input-output data set. The simulation results reveal the efficiency of the approach. DEWEY : 629.8 ISSN : 0022-0437 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA00013200 [...] [article] Identification of a variable mass underwater vehicle via volterra neural network [texte imprimé] / T. Binazadeh, Auteur ; Yazdanpanah, M. J., Auteur ; M. H. Shafiei, Auteur . - 2010 . - 07 p. Systèmes dynamiques Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 132 N° 2 (Mars/Avril 2010) . - 07 p. Mots-clés : Hydrodynamics  Mechanical  engineering  computing  Neural  nets  Polynomial  approximation  Recursive  estimation  Underwater  vehicles  Vehicle  dynamics  Volterra  equations Index. décimale : 629.8 Résumé : The first step in designing a control system for a rigid body is to understand its dynamics. Underwater vehicle dynamics may be complex and difficult to model, mainly due to difficulties in observing and measuring actual underwater vehicle hydrodynamics response. This paper is concerned with structure selection of nonlinear polynomials in a Volterra polynomial basis function neural network and recursive parameter estimation of the selected model, in order to obtain a model of a variable mass underwater vehicle with six degrees of freedom using an input-output data set. The simulation results reveal the efficiency of the approach. DEWEY : 629.8 ISSN : 0022-0437 En ligne : http://asmedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA00013200 [...]